Martin Johnsson's blog

Meny

Morning coffee: alpha level 0.005

Valen Johnson recently published a paper in PNAS about Bayes factors and p-values. In null hypothesis testing p-values measure the probability of seeing data this extreme or more extreme, if the null hypothesis is true. Bayes factors measures the ratio between the posterior probability of the alternative hypothesis to the posterior probability of the null hypothesis. The words ‘probability of the hypothesis’ tells us we’re in Bayes land, but of course, that posterior probability comes from combining the prior probability with the likelihood, which is the probability of generating the data under the hypothesis. So the Bayes factor considers not only what happens if the null is true, but what happens if the alternative is true. That is one source of discrepancies between them. Johnson has found a way to construct Bayes factors so that they correspond certain common hypothesis tests (including an approximation for the t-test, so there goes most of biology), and found for many realistic test situations a p-value of 0.05 corresponds to pretty weak support in terms of Bayes factors. Therefore, he suggests the alpha level of hypothesis tests should be reduced to at least 0.005. I don’t know enough about Bayes factors to really appreciate Johnson’s analysis. However, I do know that some responses to the paper make things seem a bit too easy. Johnson writes:

Of course, there are costs associated with raising the bar for statistical significance. To achieve 80% power in detecting a standardized effect size of 0.3 on a normal mean, for instance, decreasing the threshold for significance from 0.05 to 0.005 requires an increase in sample size from 69 to 130 in experimental designs. To obtain a highly significant result, the sample size of a design must be increased from 112 to 172.

If one does not also increase the sample sizes to preserve — or, I guess, preferably improve — power, just reducing the alpha level to 0.005 will only make matters worse. With low power comes, as Andrew Gelman likes to put it, high Type M or magnitude error rate. That is if power is bad enough not only will there be few significant findings, but all of them will be overestimates.